Unseen Object Instance Segmentation

Developing robot perception systems for handling objects in the real-world requires computer vision algorithms to be carefully scrutinized with respect to the expected operating domain. This demands large quantities of ground truth data to rigorously evaluate the performance of algorithms. The Object Cluttered Indoor Dataset is an RGBD-dataset containing point-wise labeled point-clouds for each object. The data was captured using two ASUS-PRO Xtion cameras that are positioned at different heights. It captures diverse settings of objects, background, context, sensor to scene distance, viewpoint angle and lighting conditions. The main purpose of OCID is to allow systematic comparison of existing object segmentation methods in scenes with increasing amount of clutter. In addition OCID does also provide ground-truth data for other vision tasks like object-classification and recognition. Source: OCID

Synthetic training dataset of 50,000 depth images and 320,000 object masks using simulated heaps of 3D CAD models. Source: Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data